Clinicopathologic and Prognostic Significance of Multiple Hormone Expression in Pancreatic Neuroendocrine Tumors
Bibliographic record
Abstract
Pancreatic neuroendocrine tumors (PanNETs) produce variable peptide hormones. The expression status of some hormones has been linked to the biological and clinical behaviors of PanNETs. A total of 226 surgically resected PanNETs were selected. Immunolabeling for peptide hormones was compared with various clinicopathologic factors, including patient survival. Expression of insulin, glucagon-like peptide 1, glucagon, gastrin, somatostatin, and serotonin were observed in 56 (24.8%), 41 (18.1%), 25 (11.1%), 5 (2.2%), 5 (2.2%), and 4 (1.8%) cases, respectively. Expression of 1, 2, and 3 hormones was noted in 70 (31.0%), 28 (12.4%), and 3 (1.3%) cases, respectively; 125 cases (55.3%) were negative for all hormones. PanNETs with insulin and glucagon-like peptide 1 expression were associated with a lower grade, smaller size, lower pT and pN classifications, absence of lymphovascular invasion, and lymph node metastasis and had better survival by univariate analysis, whereas PanNETs with gastrin expression were associated with a higher grade, larger size, higher pT and pN classifications, presence of lymphovascular invasion, and lymph node metastasis and had worse survival. Gastrin expression, increased age, and tumor grade were negative prognostic factors in multivariate analysis. As the number of hormones expressed increased, the survival rate of PanNET patients increased. In summary, PanNET patients showing insulin or glucagon-like peptide 1 expression and increased numbers of expressed hormones had a better survival outcome by univariate analysis, whereas gastrin expression was a negative prognostic indicator in surgically resected PanNET patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".